Statistical software and applications

Chris Brown, Univeristy of Sydney

Statistical software and applications

60min including questions / interaction

Questionnaire (5 mins)
Software (20 mins)
Statistical tests (30 mins)
Survey analysis / Questions (5 mins)

<https://redcap.sydney.edu.au/surveys/?s=EHCH7KRKT4THD3MN>
Set survey timestamp now!

Welcome survey

Survey

Slides

Then check out these slides + example analysis code (https://github.com/cstats)

Who am I

1999 - 2002: BSc (Maths / Stats), University of Sydney
2002 - 2005: SPSS (Technical Support)
2004 - 2007: Masters Biostatistics
2005 - Now: Clinical Trials Centre, University of Sydney
2013 - 2015: Cancer Registry Ireland
2016 - Now: Bean Bar You (Chocolate Subscription)

Survey results

Currently downloading and processing your results…

library(redcapAPI)

# Read token from API.txt
read.csv("API.txt", header=FALSE, stringsAsFactors=FALSE) -> api_key

# Connect to recap via API
rcon <- redcapConnection(
  url = "https://redcap.sydney.edu.au/api/",
  token = api_key[1,1]
)

# Download current responses
data <- exportRecordsTyped(rcon)

Survey results - Statistics?

Survey results - Software?

Characteristic	N = 16 ¹
SAS	0 (0%)
SPSS	16 (100%)
Stata	3 (19%)
R	3 (19%)
Python	0 (0%)
Git	0 (0%)
REDCap	0 (0%)
Other	0 (0%)
¹ n (%)

Survey results - Focus of this talk?

Survey results - Time to complete

Statistical Software

SAS
SPSS
Stata
R
Python

Other common tools:

REDCap
Git

Popularity over time

https://www.kdnuggets.com/2010/06/software-popularity-of-data-analysis-software.html

SAS

https://www.sas.com/

Powerful / reliable / just works
Was the “standard” for pharmaceutical industry
Driven with code (programming)
Good sample size program (but is complicated)
Expensive / often available via Universities

SAS screenshot

SPSS

https://www.ibm.com/products/spss-statistics

GUI (use menus and mouse to set up analyses)
Code option (SPSS Syntax) - Replicate your analysis!!!
Python integration (generate / interact with datasets)
Base + Add-Ins. ~$150-300 USD/y. Student discounts

SPSS sample dataset

SPSS screenshot

SPSS data view

STATA

https://www.stata.com/

GUI well developed
Code is more integrated than in SPSS
Output text based but can now create word doc / pdf / html
USD ~160-510

STATA screenshot

R

https://cran.r-project.org/

Open source (rebuild of a software called S+) = Free
Code based (People have created GUIs)
Huge community, great integrations (
R-Studio (Posit) fostered the “Tidyverse” which
Packages / can get messy / easy to break things
Markdown (Quarto) / Shiny = Game changers!

RStudio screenshot

R Reproducible research

R Shiny (Interactive

https://shiny.posit.co/

Python

https://www.python.org/

Not a traditional statistical software package
Pandas / NumPy / Jupyter notebooks -> data science
Powerful, open source, huge community, AI
Open-source = FREE
Packages / can get messy / easy to break things

Python - VSCode

Git

https://git-scm.com/

Version control text files (i.e. code)
Record changes over time + explanation of why
Able to roll-back to and old version
Very useful if collaborating with others

Git - Problem

Git - Solution

Git - Benefits

Backup and Restore.
Synchronization.
Short-term / long-term undo
Track changes
Track Ownership
Sandboxing
Branching and merging

https://betterexplained.com/articles/a-visual-guide-to-version-control/

Backup and Restore. Files are saved as they are edited, and you can jump to any moment in time. Need that file as it was on Feb 23, 2007? No problem.
Synchronization. Lets people share files and stay up-to-date with the latest version.
Short-term undo. Monkeying with a file and messed it up? (That’s just like you, isn’t it?). Throw away your changes and go back to the “last known good” version in the database.
Long-term undo. Sometimes we mess up bad. Suppose you made a change a year ago, and it had a bug. Jump back to the old version, and see what change was made that day.
Track Changes. As files are updated, you can leave messages explaining why the change happened (stored in the VCS, not the file). This makes it easy to see how a file is evolving over time, and why.
Track Ownership. A VCS tags every change with the name of the person who made it. Helpful for ~~blamestorming~~ giving credit.
Sandboxing, or insurance against yourself. Making a big change? You can make temporary changes in an isolated area, test and work out the kinks before “checking in” your changes.
Branching and merging. A larger sandbox. You can branch a copy of your code into a separate area and modify it in isolation (tracking changes separately). Later, you can merge your work back into the common area.

REDCap

https://project-redcap.org/

Open source database system

REDCap (example)

Statistical tests

	H0 true	H1 true
Fail to reject H0	Correct	Type 2 ($\beta$)
Reject H0	Type 1 ($\alpha$)	Correct (Power = 1-$\beta$)

Randomisation

https://heyspinner.com/random-number-wheel/1-2

Types of data

Binary
Categorical (ordered/unordered)
Continuous
Time-to-event

Common tests

Comparing proportions
Comparing continuous
Comparing time-to-event

Example dataset

From R’s “survival” package: “cancer” dataset

Survival in patients with advanced lung cancer from the North Central Cancer Treatment Group. Performance scores rate how well the patient can perform usual daily activities.

Example dataset - Variables

Variable	Description
inst	Institution code
time	Survival time in days
status	censoring status 1=censored, 2=dead
age	Age in years
sex	Male=1 Female=2
ph.ecog	ECOG as rated by the physician.
ph.karno	KPS (bad=0, good=100) rated by physician
pat.karno	rated by patient
meal.cal	Calories consumed at meals
wt.loss	Weight loss in last six months (pounds)

Proportions

Power


     Two-sample comparison of proportions power calculation 

              n = 14
             p1 = 0.2
             p2 = 0.7051759
      sig.level = 0.05
          power = 0.8
    alternative = two.sided

NOTE: n is number in *each* group

Proportions in R

tbl_cross(cancer_clean,
          row=Event,
          col=Sex,
          percent = "column"
) %>% 
add_p()

	Sex		Total	p-value ¹
	Male	Female	Total	p-value ¹
Event				<0.001
Died	112 (81%)	53 (59%)	165 (72%)
Censored	26 (19%)	37 (41%)	63 (28%)
Total	138 (100%)	90 (100%)	228 (100%)
¹ Pearson’s Chi-squared test

Proportions in SPSS

Proportions in SPSS (tidy)

Proportions in Stata

Continuous

T-Test in R

tbl_summary(cancer_clean,
          include = c(age, meal.cal, wt.loss),
          statistic = list(all_continuous() ~ "{mean} ({sd})" ),
          by=sex
) %>% 
add_p(test=list(all_continuous() ~ "t.test"))

Characteristic	1 N = 138 ¹	2 N = 90 ¹	p-value ²
age	63 (9)	61 (9)	0.064
meal.cal	981 (413)	841 (369)	0.020
Unknown	24	23
wt.loss	11 (13)	8 (13)	0.060
Unknown	10	4
¹ Mean (SD)
² Welch Two Sample t-test

T-Test in SPSS

T-Test in Stata

Time to Event

Time to event - Data

Kaplan Meier

fit <- survfit(Surv(time, status) ~ 1, data = cancer)
plot(fit)

Kaplan Meier - Stata

Kaplan Meier - SPSS

Log-rank test

Median follow-up

Competing risks

Competing risk - CI

Modelling

Regression
- UV
- MV
- Repeated measures / clustering
Time to event
- Cox-proportional hazards
- Competing risks

Cox Regression - Hazards

Proportional hazards

Hazard Ratio: The average ratio of these two lines (over the whole period)

Proportional hazards

‘Baseline hazard’ can take any form (i.e. doesn’t have to be constant / smooth)

Non-proportional hazards

Can appear as curves “crossing” or “diverging”
If so, a single number (hazard ratio) may not be the appropriate summary

Non-proportional hazards

Sub-groups

Cox regression in R

cox_model = coxph(Surv(time, status) ~ sex, data = cancer)
summary(cox_model)

Call:
coxph(formula = Surv(time, status) ~ sex, data = cancer)

  n= 228, number of events= 165 

       coef exp(coef) se(coef)      z Pr(>|z|)   
sex -0.5310    0.5880   0.1672 -3.176  0.00149 **
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

    exp(coef) exp(-coef) lower .95 upper .95
sex     0.588      1.701    0.4237     0.816

Concordance= 0.579  (se = 0.021 )
Likelihood ratio test= 10.63  on 1 df,   p=0.001
Wald test            = 10.09  on 1 df,   p=0.001
Score (logrank) test = 10.33  on 1 df,   p=0.001

Cox regression in R

tbl_regression(cox_model,
               exponentiate = TRUE) %>% 
  add_global_p()

Characteristic	HR ¹	95% CI ¹	p-value
sex	0.59	0.42, 0.82	0.001
¹ HR = Hazard Ratio, CI = Confidence Interval

test.ph <-cox.zph(cox_model)
ggcoxzph(test.ph)

Time dependent Cox

Univariable models

Characteristic	N	HR ¹	95% CI ¹	p-value
sex	228			0.001
male		—	—
female		0.59	0.42, 0.82
age	228	1.02	1.00, 1.04	0.039
age_10	228	1.21	1.01, 1.44	0.039
ph.ecog	227	1.61	1.29, 2.01	<0.001
ecog	227			<0.001
0		—	—
1		1.45	0.98, 2.13
2		2.50	1.61, 3.88
3/4		9.10	1.22, 67.9
ph.karno_10	227	0.85	0.76, 0.95	0.006
meal.cal_1000	181	0.88	0.56, 1.39	0.6
wt.loss_10	214	1.01	0.90, 1.14	0.8
¹ HR = Hazard Ratio, CI = Confidence Interval

age vs age_10?
ph.ecog vs ecog?

Multivariable models

Characteristic	HR ¹	95% CI ¹	p-value
sex
male	—	—
female	0.58	0.39, 0.85	0.006
age_10	1.13	0.90, 1.42	0.3
ecog
0	—	—
1	1.85	1.07, 3.20	0.026
2	4.81	2.06, 11.2	<0.001
3/4	16.0	1.78, 143	0.013
ph.karno_10	1.22	0.98, 1.52	0.076
meal.cal_1000	0.97	0.58, 1.60	0.9
wt.loss_10	0.89	0.76, 1.03	0.12
¹ HR = Hazard Ratio, CI = Confidence Interval

Model selection

Consider your context and objective
~~Backwards / forwards selection~~
Best subset
LASSO regression

Want to learn more? Suggest Frank Harrell, Regression Modelling Strategies. https://hbiostat.org/rmsc/

Repeated measures / clustering

Mixed effects logistic regression
Generalised estimating equations (GEE)

Katherine E. Francis, Reporting the trajectories of adverse events over the entire treatment course….

Summary

Use any package (one you can get support with)
Have a reproducible mindset (make life easy)
Use version control (keep things tidy)
Write comments for “future you” / others

GitHub Copilot

https://docs.github.com/en/copilot

Reproducible mindset

Aim to be able to run from source program to output report without manual intervention:

You won’t forget how to run / update things (+5 years)
Someone else can run it if they want to
You can’t make copy/paste / typing errors
Automatic updates (if you the data changes)

My poster: Embedding reproducible research principals in clinical trial analyses

Survey part 2

Please complete the 2nd part of the survey now… I really appreciate your feedback (use QR only if lost the page)

ACORD 2026

Want to develop an trial idea into a full protocol in 6 days?

Consider an ACORD protocol development workshop

https://www.moga.org.au/2026-acord-workshop

Thank you

Any questions?

	H0 true	H1 true
Fail to reject H0	Correct	Type 2 (\(\beta\))
Reject H0	Type 1 (\(\alpha\))	Correct (Power = 1-\(\beta\))